v2.5.MEP.Introspection.Rmd
D. Ford Hannum Jr.
9/23/2020
knitr::opts_chunk$set(echo = FALSE, message = FALSE)
library(Seurat)
library(ggplot2)
library(data.table)
library(MAST)
library(SingleR)
library(dplyr)
library(tidyr)
library(limma)
library(scRNAseq)## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.4
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## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
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## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
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## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
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## other attached packages:
## [1] scRNAseq_2.2.0 limma_3.44.3
## [3] tidyr_1.1.1 dplyr_1.0.2
## [5] SingleR_1.2.4 MAST_1.14.0
## [7] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.2
## [9] DelayedArray_0.14.1 matrixStats_0.56.0
## [11] Biobase_2.48.0 GenomicRanges_1.40.0
## [13] GenomeInfoDb_1.24.2 IRanges_2.22.2
## [15] S4Vectors_0.26.1 BiocGenerics_0.34.0
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## Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
## Please use `as_label()` or `as_name()` instead.
## This warning is displayed once per session.
This File
Going to analyze the MEP cluster, which is of greatest interest to us.
Goal
To see if this MEP cluster is playing a role in fibrogenesis.
Distinguishing MEP Cluster
Looking to find what genes distinguish this MEP cluster from all other clusters in the analysis.
MEP Up-regulated markers
This was also done for down-regulated markers, but it is not very informative as there are few genes that are widely expressed in all other clusters excluding MEP cluster.
Subclustering
Going to look at a few different subclusterings:
- MEP + Megakaryocyte + Erythrocyte
- MEP + ?GMP + ?CMP
- MEP + Megakaryocyte + Erythrocyte + ?GMP + ?CMP
- MEP
Note: the original plan was to do this in Seurat, but this is where Monocle may be more useful to look at trajectories.
MEP + Megakaryocyte + Erythrocyte
## [1] "Cells in Subset 1"##
## Gran-1 Gran-2 ?GMP B cell-1 Gran-3
## 0 0 0 0 0
## Monocyte ?MEP/Mast ?CMP/Neutro Macrophage B cell-2
## 0 592 0 0 0
## Erythrocyte T cell Megakaryocyte B cell-3 B cell-4
## 312 0 178 0 0##
## enrMigr1 enrMpl enrNbeal_cntrl Migr1 Mpl Nbeal_cntrl
## Gran-1 0 0 0 0 0 0
## Gran-2 0 0 0 0 0 0
## ?GMP 0 0 0 0 0 0
## B cell-1 0 0 0 0 0 0
## Gran-3 0 0 0 0 0 0
## Monocyte 0 0 0 0 0 0
## ?MEP/Mast 12 500 10 11 51 8
## ?CMP/Neutro 0 0 0 0 0 0
## Macrophage 0 0 0 0 0 0
## B cell-2 0 0 0 0 0 0
## Erythrocyte 65 26 29 76 110 6
## T cell 0 0 0 0 0 0
## Megakaryocyte 8 25 121 6 8 10
## B cell-3 0 0 0 0 0 0
## B cell-4 0 0 0 0 0 0## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session
MEP + ?GMP + ?CMP
MEP + Megakaryocyte + Erythrocyte + ?GMP + ?CMP
MEP
## [1] "Cells in Subset 4 (MEPs)"##
## Gran-1 Gran-2 ?GMP B cell-1 Gran-3
## 0 0 0 0 0
## Monocyte ?MEP/Mast ?CMP/Neutro Macrophage B cell-2
## 0 592 0 0 0
## Erythrocyte T cell Megakaryocyte B cell-3 B cell-4
## 0 0 0 0 0##
## enrMigr1 enrMpl enrNbeal_cntrl Migr1 Mpl Nbeal_cntrl
## Gran-1 0 0 0 0 0 0
## Gran-2 0 0 0 0 0 0
## ?GMP 0 0 0 0 0 0
## B cell-1 0 0 0 0 0 0
## Gran-3 0 0 0 0 0 0
## Monocyte 0 0 0 0 0 0
## ?MEP/Mast 12 500 10 11 51 8
## ?CMP/Neutro 0 0 0 0 0 0
## Macrophage 0 0 0 0 0 0
## B cell-2 0 0 0 0 0 0
## Erythrocyte 0 0 0 0 0 0
## T cell 0 0 0 0 0 0
## Megakaryocyte 0 0 0 0 0 0
## B cell-3 0 0 0 0 0 0
## B cell-4 0 0 0 0 0 0
Differential Expression
MEPs
##
## enrMigr1 enrMpl enrNbeal_cntrl Migr1 Mpl Nbeal_cntrl
## 0 6 166 2 2 9 0
## 1 0 165 0 0 14 0
## 2 0 75 0 2 5 0
## 3 4 32 8 6 16 8
## 4 0 55 0 0 3 0
## 5 2 7 0 1 4 0
Nbeal vs Migr1
##
## Control Migr1 Mpl
## 18 23 551## [1] 9 5
Mpl vs Migr1
## [1] 113 5## p_val avg_logFC pct.1 pct.2 p_val_adj
## Slpi 2.923376e-23 2.864115 0.964 0.739 5.294527e-19
## Akr1c18 2.491825e-15 2.638171 0.835 0.043 4.512945e-11
## Ccl4 6.282744e-12 2.556199 0.911 0.391 1.137868e-07
## Furin 1.340648e-30 1.838023 0.989 0.783 2.428047e-26
## Cfp 5.628861e-16 1.594472 0.940 0.348 1.019443e-11
## Ccl6 4.286424e-21 1.450810 0.978 0.739 7.763142e-17
Mpl vs Migr1 & Nbeal Controls
## [1] 185 5## p_val avg_logFC pct.1 pct.2 p_val_adj
## Slpi 7.031209e-39 2.962248 0.964 0.707 1.273422e-34
## Ccl4 9.744238e-24 2.864216 0.911 0.317 1.764779e-19
## Akr1c18 2.707028e-27 2.753610 0.835 0.024 4.902698e-23
## Furin 4.255144e-54 2.032867 0.989 0.659 7.706491e-50
## Ccl6 3.703546e-53 1.983642 0.978 0.634 6.707492e-49
## S100a6 1.539080e-71 1.865602 0.998 0.561 2.787428e-67
GO Terms
Looking at the GO terms that are most associated with up- and down-regulated genes from Mpl compared to Nbeal and Migr1. These will be located in a supplementary excel file. See MEP.mpl.vs.migr1ANDnbeal.xlsx